arXiv:2606.11277v1 Announce Type: new Abstract: Reliable extrapolation remains a central challenge for generative models in computational physics, because models trained over finite ranges of time, parameters, or geometries may produce physically inconsistent predictions outside the training distribution. We introduce a least-action-principle-guided diffusion, LAPG, a framework that promotes physical consistency during inference rather than relying solely on constraints imposed during training. The method combines a conditional score-based diffusion model with an action-derived physical guidan
The increasing complexity of generative AI models necessitates novel methods to ensure physical consistency, especially as their application expands into scientific domains like computational physics.
This development offers a potential breakthrough for integrating physical laws directly into AI models post-training, enhancing reliability and accelerating discovery in scientific and engineering fields.
The paradigm for developing reliable physics-based AI shifts from solely relying on training data constraints to actively enforcing physical consistency during inference through mechanisms like the least-action principle.
- · Computational Physics Researchers
- · Generative AI Developers
- · Engineering Design firms
- · Scientific Computing sector
- · Traditional simulation methods (in specialized domains)
- · AI models without physics-informed guidance
Increased accuracy and reliability of AI models for physical extrapolation tasks.
Accelerated discovery of new materials, drug compounds, or engineering designs due to more robust AI predictions.
Reduced reliance on extensive, controlled laboratory experiments as AI simulations become more trustworthy predictors of physical outcomes.
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Read at arXiv cs.LG